nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False)
nn.Conv2d(inp if (self.stride > 1) else branch_features,branch_features, kernel_size=1, stride=1, padding=0, bias=False)
nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False)
nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False)
nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False)
nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
nn.Conv2d(in_ch, mid_ch, 1, bias=False)
nn.Conv2d(mid_ch, mid_ch, kernel_size, padding=kernel_size // 2,stride=stride, groups=mid_ch, bias=False)
nn.Conv2d(mid_ch, out_ch, 1, bias=False)
nn.Conv2d(3, depths[0], 3, padding=1, stride=2, bias=False)
nn.Conv2d(depths[0], depths[0], 3, padding=1, stride=1,groups=depths[0], bias=False)
nn.Conv2d(depths[0], depths[1], 1, padding=0, stride=1, bias=False)
nn.Conv2d(depths[7], 1280, 1, padding=0, stride=1, bias=False)
nn.Conv2d(3, 32, 3, padding=1, stride=2, bias=False)
nn.Conv2d(32, 32, 3, padding=1, stride=1, groups=32,bias=False)
nn.Conv2d(32, 16, 1, padding=0, stride=1, bias=False)
nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
nn.Conv2d(squeeze_planes, expand1x1_planes,kernel_size=1)
nn.Conv2d(squeeze_planes, expand3x3_planes,kernel_size=3, padding=1)
nn.Conv2d(3, 96, kernel_size=7, stride=2)
nn.Conv2d(3, 64, kernel_size=3, stride=2)
nn.Conv2d(512, self.num_classes, kernel_size=1)
nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False)
nn.Conv2d(inter_channels, channels, 1)
nn.Conv2d(256, 256, 3, padding=1, bias=False)
nn.Conv2d(256, num_classes, 1)
nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False)
nn.Conv2d(in_channels, out_channels, 1, bias=False)
nn.Conv2d(in_channels, out_channels, 1, bias=False)
nn.Conv2d(5 * out_channels, out_channels, 1, bias=False)
nn.Conv2d(num_input_features, bn_size *growth_rate, kernel_size=1, stride=1,   bias=False)
nn.Conv2d(bn_size * growth_rate, growth_rate,   kernel_size=3, stride=1, padding=1,   bias=False)
nn.Conv2d(num_input_features, num_output_features,kernel_size=1, stride=1, bias=False)
nn.Conv2d(3, num_init_features, kernel_size=7, stride=2,padding=3, bias=False)
nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False)
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False)
nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=dilation, groups=groups, bias=False, dilation=dilation)
nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,bias=False)
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)
nn.Conv2d(64, 192, kernel_size=5, padding=2)
nn.Conv2d(192, 384, kernel_size=3, padding=1)
nn.Conv2d(384, 256, kernel_size=3, padding=1)
nn.Conv2d(256, 256, kernel_size=3, padding=1)
nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1)
nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1, stride=1)